Last week, I wrote about the limitation of using human-level performance (HLP) as a metric to beat in machine learning applications for manufacturing and other fields. In this letter, I would like to show why beating HLP isn’t always the best way to improve performance.

Beating human-level performance (HLP) has been a goal of academic research in machine learning from speech recognition to X-ray diagnosis. When your model outperforms humans, you can argue that you’ve reached a significant milestone and publish a paper!

As I write this letter, the vote count is underway in yesterday’s U.S. presidential election. The race has turned out to be tight. In their final forecast last night, the political analysts at fivethirtyeight.com suggested an 89 percent chance that Joe Biden would win.

Welcome to this special Halloween issue of The Batch! In AI, we use many challenging technical terms. To help you keep things straight, I would like to offer some definitions that I definitely would not use.

Today Landing AI, where I am CEO, launched LandingLens, an AI-powered platform that helps manufacturers develop computer vision solutions that can identify defective products.

My father recently celebrated a milestone: He has completed 146 online courses since 2012. His studies have spanned topics from creative writing to complexity theory. Ronald Ng is a great example of lifelong learning.

There’s a lot we don’t know about the future: When will a Covid-19 vaccine be available? Who will win the next election? Or in a business context, how many customers will we have next year?

This special issue of The Batch celebrates the launch of our new Generative Adversarial Networks Specialization! GANs are among the most exciting technologies to emerge from deep learning.

AI researchers keep coming up with impressive innovations: transformer-based language models, self-supervised learning, deep reinforcement learning, small data. All of these developments hold great promise.

I’d like to share a programming tip that I’ve used for years. A large part of programming involves googling for code snippets you need on Stack Overflow and other websites. (Shh. Don’t tell the nondevelopers. ????)

Today we take it for granted that many people know how to read and write. Someday, I hope, it will be just as common that people know how to write code. Several hundred years ago, society didn’t view language literacy as a necessary skill.

Did you ever spend days obsessing over a technical problem? If so, I applaud you. Determined pursuit of solutions to hard problems is an important step toward building deep expertise.

I’ve been trying to teach my toddler the alphabet. Despite having some educational experience, when she mispronounces a vowel for the nth time, I can’t help but feel like I’m doing it wrong.

Last week, I asked what values the AI community stands for. Thank you to everyone who replied! The email responses in aggregate ran to 55 pages of text, and I enjoyed reading all of them.

Earlier this week, I asked a question on social media: What is the most important problem that the AI community should work on? Thousands of you responded. The most frequently mentioned themes included...